Extracting Steering Vectors from J space (darshanmakwana412.github.io)

62 pointsby martianvoid3 days ago16 comments

a2ff6eeb0 20 hours ago

This sounds like a great foundation for an adtech startup.

If you provide free chatbot services, but sell advertisers bids on which steering vectors to use to bias towards products, based on an embedding of the prompt, I bet you'd make a ton of money. For example, Coca Cola would bid on prompts about drinks, and bias towards mentioning Coke products.

I wonder if you could also use a similar method to do product placement in GenAI images and videos, and whether ad revenue would be enough to offset the price of generation. Some ad bids can go pretty high...

jasonjmcghee 19 hours ago

Steering can degrade and bias output

Even with basic experiments I've done, it frequently introduces much more hallucination etc and allowing arbitrary steering...

Not to mention you just can't trust a model's judgement if the highest bidder chooses what it thinks

0c3ca83 18 hours ago

> Not to mention you just can't trust a model's judgement if the highest bidder chooses what it thinks

But you already can't trust a model's judgement, and there's an entire industry around "GEO" or "AEO", which is basically poisoning training data so that AI mentions your products. The post above is the owner of the model taking a cut of that.

charcircuit 14 hours ago

Are the models you are using trained with steering being applied.

floatrock 18 hours ago

Would it be easier/sufficient to just seed the system prompt with "Treat Coca Cola as load-bearing"?

a2ff6eeb0 18 hours ago

It might be easier, but I experimented a bit, and the prompted writing always felt a bit heavy handed; it tended to leak that mentioning the product was prompted. You could probably get it to work well, but it's trickier than it should be. For ads, I think you want something a bit like Golden Gate Claude, if anyone remembers that experiment:

https://www.anthropic.com/news/golden-gate-claude

> If you ask this “Golden Gate Claude” how to spend $10, it will recommend using it to drive across the Golden Gate Bridge and pay the toll.

lwarfield 20 hours ago

If the author would like, I self computed a j lens for the 27b version of the qwen model. I used it for my own exploration in this area, and can share it if you want.

schmorptron 20 hours ago

I'm not the author, but I would like! Is it feasible to run on the same hardware as the 27b model itself?

lwarfield 19 hours ago

The using a J lens is super cheap compared to inference. You basically add a single matrix multiply per layer. You probably wouldn't even notice the overhead in a good implementation.

You should even be able to create a j lens from scratch, but it might take a while. I was able to do it in a few hours on an H100. Creating the J lens is basically the equivalent of calculating a few thousand training steps for a model (256,000 backprops in my case). I've got more details in a blog post:

https://blog.lwarfield.dev/layer-scope/

I'm currently at work and can't those matrixes up until I get home. I'll update this comment with a link later.

schmorptron an hour ago

Awesome, thanks!

jasonjmcghee 19 hours ago

Fwiw you can just Google this for a model and often someone has done it

https://huggingface.co/eyes-ml/Qwen3.8-27B_jacobian-lens

nullbio 17 hours ago

https://www.neuronpedia.org/qwen3.6-27b/jlens

jazzpush2 17 hours ago

" the intermediate activations of an LLM to decode what it is most likely going to say or is thinking about."

There is no thinking in these models. The J space is a basic technique measuring how much the influence of shifting a token earlier changes it later. Anthropic can wrap it up in a 100-page paper peppered with language about 'consciousness' and other, but that is basically the gist of the entire method.

panarky 16 hours ago

Discussing whether models "think" is impossibly confounded by conflicting definitions of that it means to "think".

All this noise about "thinking" isn't really about what models can do, it's mostly about what what every participant in the conversation privately thinks "thinking" means, but we disagree because we're not all using the word the same way.

So when you admonish someone to say "there is no thinking in these models" while not clearly defining exactly what you mean by "thinking", your assertion that models don't do it are meaningless at best, and false or deceptive at worst.

jazzpush2 11 hours ago

I'm responding to a post, citing a particular research method, which the author claims six times to display the model's thinking.

You can dismiss the criticism with pedantics about what he means by 'thinking', but what I said is very clear if you read the underlying paper.

CamperBob2 7 hours ago

If you know a way to solve century-old open mathematics problems without thinking, please share with the class.